(2) #52 Republic (12-14)

1229.21 (31)

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# Opponent Result Effect % of Ranking Status Date Event
59 Green River Swordfish Loss 8-11 -13.69 3% Jul 8th San Diego Slammer 2017
36 Voodoo Loss 5-11 -12.52 3% Jul 8th San Diego Slammer 2017
157 Brawl** Win 11-0 0 0% Ignored Jul 8th San Diego Slammer 2017
51 Sundowners Loss 6-9 -12.76 3% Jul 8th San Diego Slammer 2017
70 Choice City Hops Loss 6-11 -22.22 3% Jul 8th San Diego Slammer 2017
126 ISO Atmo Win 15-7 1.57 3% Jul 9th San Diego Slammer 2017
113 Thundersnow Win 15-12 -3.92 3% Jul 9th San Diego Slammer 2017
113 Thundersnow Win 14-13 -13.02 4.13% Aug 19th Ski Town Classic 2017
- Big Sky** Win 13-2 0 0% Ignored Aug 19th Ski Town Classic 2017
69 Sawtooth Win 13-4 19.42 4.13% Aug 19th Ski Town Classic 2017
106 Syndicate Win 13-7 8.28 4.13% Aug 20th Ski Town Classic 2017
38 Sprawl Loss 12-14 -1.51 4.13% Aug 20th Ski Town Classic 2017
69 Sawtooth Loss 8-11 -22.14 4.13% Aug 20th Ski Town Classic 2017
16 SoCal Condors Loss 9-13 5.81 4.84% Sep 9th 2017 So Cal Mens Sectionals
51 Sundowners Loss 10-12 -11.84 4.84% Sep 9th 2017 So Cal Mens Sectionals
157 Brawl** Win 13-1 0 0% Ignored Sep 9th 2017 So Cal Mens Sectionals
55 Streetgang Win 13-3 29.25 4.84% Sep 9th 2017 So Cal Mens Sectionals
16 SoCal Condors Loss 8-13 1.86 4.84% Sep 10th 2017 So Cal Mens Sectionals
113 Thundersnow Win 13-6 8.78 4.84% Sep 10th 2017 So Cal Mens Sectionals
55 Streetgang Loss 11-12 -7.65 4.84% Sep 10th 2017 So Cal Mens Sectionals
55 Streetgang Win 13-6 32.73 5.39% Sep 23rd Southwest Mens Regionals 2017
18 Guerrilla Loss 8-13 -5.59 5.39% Sep 23rd Southwest Mens Regionals 2017
38 Sprawl Loss 10-11 3.47 5.39% Sep 23rd Southwest Mens Regionals 2017
59 Green River Swordfish Win 15-9 24.95 5.39% Sep 23rd Southwest Mens Regionals 2017
18 Guerrilla Loss 11-15 0.96 5.39% Sep 24th Southwest Mens Regionals 2017
55 Streetgang Loss 13-14 -8.56 5.39% Sep 24th Southwest Mens Regionals 2017
**Blowout Eligible

FAQ

The results on this page ("USAU") are the results of an implementation of the USA Ultimate Top 20 algorithm, which is used to allocate post season bids to both colleg and club ultimate teams. The data was obtained by scraping USAU's score reporting website. Learn more about the algorithm here. TL;DR, here is the rating function. Every game a team plays gets a rating equal to the opponents rating +/- the score value. With all these data points, we iterate team ratings until convergence. There is also a rule for discounting blowout games (see next FAQ)
For reference, here is handy table with frequent game scrores and the resulting game value:
"...if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5."

Translation: if a team plays a game where even earning the max point win would hurt them, they can have the game ignored provided they win by enough and have suffficient unignored results.